Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations74612
Missing cells9
Missing cells (%)< 0.1%
Duplicate rows8
Duplicate rows (%)< 0.1%
Total size in memory12.5 MiB
Average record size in memory176.0 B

Variable types

DateTime1
Text2
Categorical11
Numeric7
Unsupported1

Alerts

Dataset has 8 (< 0.1%) duplicate rowsDuplicates
Classification_Of_Accident is highly overall correlated with Num_of_Fatal_InjuriesHigh correlation
Lat is highly overall correlated with LongHigh correlation
Location_Type is highly overall correlated with Traffic_ControlHigh correlation
Long is highly overall correlated with LatHigh correlation
Num_of_Fatal_Injuries is highly overall correlated with Classification_Of_Accident and 4 other fieldsHigh correlation
Num_of_Injuries is highly overall correlated with Num_of_Fatal_Injuries and 2 other fieldsHigh correlation
Num_of_Major_Injuries is highly overall correlated with Num_of_Fatal_InjuriesHigh correlation
Num_of_Minimal_Injuries is highly overall correlated with Num_of_Fatal_Injuries and 2 other fieldsHigh correlation
Num_of_Minor_Injuries is highly overall correlated with Num_of_Fatal_Injuries and 2 other fieldsHigh correlation
Traffic_Control is highly overall correlated with Location_TypeHigh correlation
Classification_Of_Accident is highly imbalanced (56.2%) Imbalance
Road_Surface_Condition is highly imbalanced (53.9%) Imbalance
Environment_Condition is highly imbalanced (65.3%) Imbalance
Traffic_Control is highly imbalanced (56.8%) Imbalance
Num_Of_Pedestrians is highly imbalanced (92.3%) Imbalance
Num_of_Bicycles is highly imbalanced (93.4%) Imbalance
Num_of_Motorcycles is highly imbalanced (96.4%) Imbalance
Num_of_Fatal_Injuries is highly imbalanced (99.0%) Imbalance
Num_of_Major_Injuries is highly skewed (γ1 = 31.27932899) Skewed
Lat is highly skewed (γ1 = -23.8028575) Skewed
Max_Injury is an unsupported type, check if it needs cleaning or further analysis Unsupported
Num_of_Injuries has 61195 (82.0%) zeros Zeros
Num_of_Minimal_Injuries has 68879 (92.3%) zeros Zeros
Num_of_Minor_Injuries has 68879 (92.3%) zeros Zeros
Num_of_Major_Injuries has 73941 (99.1%) zeros Zeros

Reproduction

Analysis started2024-10-16 22:09:20.146222
Analysis finished2024-10-16 22:09:42.122720
Duration21.98 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

Distinct2184
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size583.0 KiB
Minimum2017-01-01 00:00:00
Maximum2022-12-30 00:00:00
2024-10-16T22:09:42.459558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:42.760832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1439
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size583.0 KiB
2024-10-16T22:09:43.387507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.8502386
Min length4

Characters and Unicode

Total characters361886
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row1:28
2nd row3:16
3rd row7:17
4th row8:58
5th row11:41
ValueCountFrequency (%)
unknown 2757
 
3.7%
16:30 614
 
0.8%
17:00 590
 
0.8%
16:00 568
 
0.8%
15:30 543
 
0.7%
15:00 503
 
0.7%
17:30 478
 
0.6%
18:00 450
 
0.6%
14:30 432
 
0.6%
14:00 426
 
0.6%
Other values (1429) 67251
90.1%
2024-10-16T22:09:44.243505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 71855
19.9%
1 70546
19.5%
0 41764
11.5%
2 29516
8.2%
5 29513
8.2%
3 24368
 
6.7%
4 21926
 
6.1%
7 14473
 
4.0%
8 13514
 
3.7%
6 13495
 
3.7%
Other values (6) 30916
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 361886
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 71855
19.9%
1 70546
19.5%
0 41764
11.5%
2 29516
8.2%
5 29513
8.2%
3 24368
 
6.7%
4 21926
 
6.1%
7 14473
 
4.0%
8 13514
 
3.7%
6 13495
 
3.7%
Other values (6) 30916
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 361886
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 71855
19.9%
1 70546
19.5%
0 41764
11.5%
2 29516
8.2%
5 29513
8.2%
3 24368
 
6.7%
4 21926
 
6.1%
7 14473
 
4.0%
8 13514
 
3.7%
6 13495
 
3.7%
Other values (6) 30916
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 361886
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 71855
19.9%
1 70546
19.5%
0 41764
11.5%
2 29516
8.2%
5 29513
8.2%
3 24368
 
6.7%
4 21926
 
6.1%
7 14473
 
4.0%
8 13514
 
3.7%
6 13495
 
3.7%
Other values (6) 30916
8.5%
Distinct13305
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Memory size583.0 KiB
2024-10-16T22:09:44.749222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length150
Median length106
Mean length49.927974
Min length25

Characters and Unicode

Total characters3725226
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5991 ?
Unique (%)8.0%

Sample

1st rowWEST RIDGE DR btwn PARLOR PL & BERT G. ARGUE DR (__5RG32N)
2nd rowVANIER PKWY SB btwn DONALD ST & MCARTHUR AVE (__3Z07B5)
3rd rowOLD RICHMOND RD @ ROBERTSON RD (0000639)
4th rowMERIVALE RD @ WOODFIELD DR/ROYDON PL (0009776)
5th rowBANK ST @ BELANGER AVE/LAMIRA ST (0007208)
ValueCountFrequency (%)
74827
 
11.7%
rd 50463
 
7.9%
btwn 34169
 
5.3%
st 32930
 
5.1%
ave 25830
 
4.0%
dr 24418
 
3.8%
hwy417 14149
 
2.2%
417 8653
 
1.4%
highway 7985
 
1.2%
blvd 5377
 
0.8%
Other values (20243) 361073
56.4%
2024-10-16T22:09:45.549469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
565492
 
15.2%
R 236511
 
6.3%
A 196321
 
5.3%
E 195423
 
5.2%
0 162570
 
4.4%
D 148473
 
4.0%
N 125223
 
3.4%
I 120553
 
3.2%
L 112787
 
3.0%
S 111854
 
3.0%
Other values (56) 1750019
47.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3725226
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
565492
 
15.2%
R 236511
 
6.3%
A 196321
 
5.3%
E 195423
 
5.2%
0 162570
 
4.4%
D 148473
 
4.0%
N 125223
 
3.4%
I 120553
 
3.2%
L 112787
 
3.0%
S 111854
 
3.0%
Other values (56) 1750019
47.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3725226
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
565492
 
15.2%
R 236511
 
6.3%
A 196321
 
5.3%
E 195423
 
5.2%
0 162570
 
4.4%
D 148473
 
4.0%
N 125223
 
3.4%
I 120553
 
3.2%
L 112787
 
3.0%
S 111854
 
3.0%
Other values (56) 1750019
47.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3725226
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
565492
 
15.2%
R 236511
 
6.3%
A 196321
 
5.3%
E 195423
 
5.2%
0 162570
 
4.4%
D 148473
 
4.0%
N 125223
 
3.4%
I 120553
 
3.2%
L 112787
 
3.0%
S 111854
 
3.0%
Other values (56) 1750019
47.0%

Location_Type
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size583.0 KiB
Intersection
40441 
Midblock
34171 

Length

Max length12
Median length12
Mean length10.168069
Min length8

Characters and Unicode

Total characters758660
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMidblock
2nd rowMidblock
3rd rowIntersection
4th rowIntersection
5th rowIntersection

Common Values

ValueCountFrequency (%)
Intersection 40441
54.2%
Midblock 34171
45.8%

Length

2024-10-16T22:09:45.842624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T22:09:46.102518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
intersection 40441
54.2%
midblock 34171
45.8%

Most occurring characters

ValueCountFrequency (%)
n 80882
10.7%
t 80882
10.7%
e 80882
10.7%
c 74612
9.8%
i 74612
9.8%
o 74612
9.8%
I 40441
 
5.3%
r 40441
 
5.3%
s 40441
 
5.3%
M 34171
 
4.5%
Other values (4) 136684
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 758660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 80882
10.7%
t 80882
10.7%
e 80882
10.7%
c 74612
9.8%
i 74612
9.8%
o 74612
9.8%
I 40441
 
5.3%
r 40441
 
5.3%
s 40441
 
5.3%
M 34171
 
4.5%
Other values (4) 136684
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 758660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 80882
10.7%
t 80882
10.7%
e 80882
10.7%
c 74612
9.8%
i 74612
9.8%
o 74612
9.8%
I 40441
 
5.3%
r 40441
 
5.3%
s 40441
 
5.3%
M 34171
 
4.5%
Other values (4) 136684
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 758660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 80882
10.7%
t 80882
10.7%
e 80882
10.7%
c 74612
9.8%
i 74612
9.8%
o 74612
9.8%
I 40441
 
5.3%
r 40441
 
5.3%
s 40441
 
5.3%
M 34171
 
4.5%
Other values (4) 136684
18.0%

Classification_Of_Accident
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size583.0 KiB
03 - P.D. only
61195 
02 - Non-fatal injury
13276 
01 - Fatal injury
 
141

Length

Max length21
Median length14
Mean length15.251206
Min length14

Characters and Unicode

Total characters1137923
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row03 - P.D. only
2nd row03 - P.D. only
3rd row03 - P.D. only
4th row03 - P.D. only
5th row03 - P.D. only

Common Values

ValueCountFrequency (%)
03 - P.D. only 61195
82.0%
02 - Non-fatal injury 13276
 
17.8%
01 - Fatal injury 141
 
0.2%

Length

2024-10-16T22:09:46.309359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T22:09:46.581381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
74612
25.0%
03 61195
20.5%
p.d 61195
20.5%
only 61195
20.5%
injury 13417
 
4.5%
02 13276
 
4.4%
non-fatal 13276
 
4.4%
01 141
 
< 0.1%
fatal 141
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
223836
19.7%
. 122390
10.8%
n 87888
 
7.7%
- 87888
 
7.7%
l 74612
 
6.6%
y 74612
 
6.6%
0 74612
 
6.6%
o 74471
 
6.5%
D 61195
 
5.4%
P 61195
 
5.4%
Other values (12) 195224
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1137923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
223836
19.7%
. 122390
10.8%
n 87888
 
7.7%
- 87888
 
7.7%
l 74612
 
6.6%
y 74612
 
6.6%
0 74612
 
6.6%
o 74471
 
6.5%
D 61195
 
5.4%
P 61195
 
5.4%
Other values (12) 195224
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1137923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
223836
19.7%
. 122390
10.8%
n 87888
 
7.7%
- 87888
 
7.7%
l 74612
 
6.6%
y 74612
 
6.6%
0 74612
 
6.6%
o 74471
 
6.5%
D 61195
 
5.4%
P 61195
 
5.4%
Other values (12) 195224
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1137923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
223836
19.7%
. 122390
10.8%
n 87888
 
7.7%
- 87888
 
7.7%
l 74612
 
6.6%
y 74612
 
6.6%
0 74612
 
6.6%
o 74471
 
6.5%
D 61195
 
5.4%
P 61195
 
5.4%
Other values (12) 195224
17.2%
Distinct8
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size583.0 KiB
03 - Rear end
24776 
07 - SMV other
11885 
04 - Sideswipe
10573 
02 - Angle
10469 
05 - Turning movement
7754 
Other values (3)
9150 

Length

Max length27
Median length21
Mean length14.854853
Min length10

Characters and Unicode

Total characters1108276
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row07 - SMV other
2nd row04 - Sideswipe
3rd row07 - SMV other
4th row03 - Rear end
5th row99 - Other

Common Values

ValueCountFrequency (%)
03 - Rear end 24776
33.2%
07 - SMV other 11885
15.9%
04 - Sideswipe 10573
14.2%
02 - Angle 10469
14.0%
05 - Turning movement 7754
 
10.4%
06 - SMV unattended vehicle 6240
 
8.4%
99 - Other 1798
 
2.4%
01 - Approaching 1112
 
1.5%
(Missing) 5
 
< 0.1%

Length

2024-10-16T22:09:46.793739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T22:09:47.076875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
74607
26.6%
03 24776
 
8.8%
rear 24776
 
8.8%
end 24776
 
8.8%
smv 18125
 
6.5%
other 13683
 
4.9%
07 11885
 
4.2%
04 10573
 
3.8%
sideswipe 10573
 
3.8%
angle 10469
 
3.7%
Other values (10) 56473
20.1%

Most occurring characters

ValueCountFrequency (%)
206109
18.6%
e 135318
 
12.2%
- 74607
 
6.7%
0 72809
 
6.6%
n 72099
 
6.5%
d 47829
 
4.3%
r 47325
 
4.3%
i 36252
 
3.3%
t 33917
 
3.1%
a 32128
 
2.9%
Other values (26) 349883
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1108276
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
206109
18.6%
e 135318
 
12.2%
- 74607
 
6.7%
0 72809
 
6.6%
n 72099
 
6.5%
d 47829
 
4.3%
r 47325
 
4.3%
i 36252
 
3.3%
t 33917
 
3.1%
a 32128
 
2.9%
Other values (26) 349883
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1108276
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
206109
18.6%
e 135318
 
12.2%
- 74607
 
6.7%
0 72809
 
6.6%
n 72099
 
6.5%
d 47829
 
4.3%
r 47325
 
4.3%
i 36252
 
3.3%
t 33917
 
3.1%
a 32128
 
2.9%
Other values (26) 349883
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1108276
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
206109
18.6%
e 135318
 
12.2%
- 74607
 
6.7%
0 72809
 
6.6%
n 72099
 
6.5%
d 47829
 
4.3%
r 47325
 
4.3%
i 36252
 
3.3%
t 33917
 
3.1%
a 32128
 
2.9%
Other values (26) 349883
31.6%

Road_Surface_Condition
Categorical

Imbalance 

Distinct11
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size583.0 KiB
01 - Dry
49509 
02 - Wet
12711 
03 - Loose snow
 
4559
06 - Ice
 
3031
04 - Slush
 
2608
Other values (6)
 
2193

Length

Max length25
Median length8
Mean length8.7346504
Min length8

Characters and Unicode

Total characters651701
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row03 - Loose snow
2nd row03 - Loose snow
3rd row01 - Dry
4th row03 - Loose snow
5th row02 - Wet

Common Values

ValueCountFrequency (%)
01 - Dry 49509
66.4%
02 - Wet 12711
 
17.0%
03 - Loose snow 4559
 
6.1%
06 - Ice 3031
 
4.1%
04 - Slush 2608
 
3.5%
05 - Packed snow 2039
 
2.7%
08 - Loose sand or gravel 63
 
0.1%
00 - Unknown 38
 
0.1%
99 - Other 36
 
< 0.1%
07 - Mud 10
 
< 0.1%

Length

2024-10-16T22:09:47.388423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
74611
32.4%
01 49509
21.5%
dry 49509
21.5%
02 12711
 
5.5%
wet 12711
 
5.5%
snow 6598
 
2.9%
loose 4622
 
2.0%
03 4559
 
2.0%
06 3031
 
1.3%
ice 3031
 
1.3%
Other values (17) 9735
 
4.2%

Most occurring characters

ValueCountFrequency (%)
156016
23.9%
0 74613
11.4%
- 74611
11.4%
r 49671
 
7.6%
D 49509
 
7.6%
y 49509
 
7.6%
1 49509
 
7.6%
e 22509
 
3.5%
o 15943
 
2.4%
s 13891
 
2.1%
Other values (31) 95920
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 651701
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
156016
23.9%
0 74613
11.4%
- 74611
11.4%
r 49671
 
7.6%
D 49509
 
7.6%
y 49509
 
7.6%
1 49509
 
7.6%
e 22509
 
3.5%
o 15943
 
2.4%
s 13891
 
2.1%
Other values (31) 95920
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 651701
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
156016
23.9%
0 74613
11.4%
- 74611
11.4%
r 49671
 
7.6%
D 49509
 
7.6%
y 49509
 
7.6%
1 49509
 
7.6%
e 22509
 
3.5%
o 15943
 
2.4%
s 13891
 
2.1%
Other values (31) 95920
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 651701
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
156016
23.9%
0 74613
11.4%
- 74611
11.4%
r 49671
 
7.6%
D 49509
 
7.6%
y 49509
 
7.6%
1 49509
 
7.6%
e 22509
 
3.5%
o 15943
 
2.4%
s 13891
 
2.1%
Other values (31) 95920
14.7%

Environment_Condition
Categorical

Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size583.0 KiB
01 - Clear
58471 
03 - Snow
7739 
02 - Rain
6562 
04 - Freezing Rain
 
1112
05 - Drifting Snow
 
290
Other values (4)
 
436

Length

Max length27
Median length10
Mean length10.012572
Min length9

Characters and Unicode

Total characters747038
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row02 - Rain
2nd row03 - Snow
3rd row05 - Drifting Snow
4th row03 - Snow
5th row01 - Clear

Common Values

ValueCountFrequency (%)
01 - Clear 58471
78.4%
03 - Snow 7739
 
10.4%
02 - Rain 6562
 
8.8%
04 - Freezing Rain 1112
 
1.5%
05 - Drifting Snow 290
 
0.4%
07 - Fog, mist, smoke, dust 187
 
0.3%
00 - Unknown 119
 
0.2%
06 - Strong wind 101
 
0.1%
99 - Other 29
 
< 0.1%
(Missing) 2
 
< 0.1%

Length

2024-10-16T22:09:47.671086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T22:09:47.990114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
74610
33.0%
01 58471
25.9%
clear 58471
25.9%
snow 8029
 
3.6%
03 7739
 
3.4%
rain 7674
 
3.4%
02 6562
 
2.9%
04 1112
 
0.5%
freezing 1112
 
0.5%
05 290
 
0.1%
Other values (13) 1824
 
0.8%

Most occurring characters

ValueCountFrequency (%)
151284
20.3%
0 74700
10.0%
- 74610
10.0%
a 66145
8.9%
e 60911
8.2%
r 60003
 
8.0%
C 58471
 
7.8%
l 58471
 
7.8%
1 58471
 
7.8%
n 17664
 
2.4%
Other values (27) 66308
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 747038
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
151284
20.3%
0 74700
10.0%
- 74610
10.0%
a 66145
8.9%
e 60911
8.2%
r 60003
 
8.0%
C 58471
 
7.8%
l 58471
 
7.8%
1 58471
 
7.8%
n 17664
 
2.4%
Other values (27) 66308
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 747038
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
151284
20.3%
0 74700
10.0%
- 74610
10.0%
a 66145
8.9%
e 60911
8.2%
r 60003
 
8.0%
C 58471
 
7.8%
l 58471
 
7.8%
1 58471
 
7.8%
n 17664
 
2.4%
Other values (27) 66308
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 747038
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
151284
20.3%
0 74700
10.0%
- 74610
10.0%
a 66145
8.9%
e 60911
8.2%
r 60003
 
8.0%
C 58471
 
7.8%
l 58471
 
7.8%
1 58471
 
7.8%
n 17664
 
2.4%
Other values (27) 66308
8.9%

Light
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size583.0 KiB
01 - Daylight
49858 
07 - Dark
16805 
05 - Dusk
 
3445
00 - Unknown
 
2691
03 - Dawn
 
1803

Length

Max length13
Median length13
Mean length11.781255
Min length9

Characters and Unicode

Total characters879023
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row07 - Dark
2nd row07 - Dark
3rd row03 - Dawn
4th row01 - Daylight
5th row01 - Daylight

Common Values

ValueCountFrequency (%)
01 - Daylight 49858
66.8%
07 - Dark 16805
 
22.5%
05 - Dusk 3445
 
4.6%
00 - Unknown 2691
 
3.6%
03 - Dawn 1803
 
2.4%
99 - Other 10
 
< 0.1%

Length

2024-10-16T22:09:48.298936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T22:09:48.587948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
74612
33.3%
01 49858
22.3%
daylight 49858
22.3%
07 16805
 
7.5%
dark 16805
 
7.5%
05 3445
 
1.5%
dusk 3445
 
1.5%
00 2691
 
1.2%
unknown 2691
 
1.2%
03 1803
 
0.8%
Other values (3) 1823
 
0.8%

Most occurring characters

ValueCountFrequency (%)
149224
17.0%
0 77293
8.8%
- 74612
 
8.5%
D 71911
 
8.2%
a 68466
 
7.8%
h 49868
 
5.7%
t 49868
 
5.7%
1 49858
 
5.7%
y 49858
 
5.7%
l 49858
 
5.7%
Other values (16) 188207
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 879023
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
149224
17.0%
0 77293
8.8%
- 74612
 
8.5%
D 71911
 
8.2%
a 68466
 
7.8%
h 49868
 
5.7%
t 49868
 
5.7%
1 49858
 
5.7%
y 49858
 
5.7%
l 49858
 
5.7%
Other values (16) 188207
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 879023
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
149224
17.0%
0 77293
8.8%
- 74612
 
8.5%
D 71911
 
8.2%
a 68466
 
7.8%
h 49868
 
5.7%
t 49868
 
5.7%
1 49858
 
5.7%
y 49858
 
5.7%
l 49858
 
5.7%
Other values (16) 188207
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 879023
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
149224
17.0%
0 77293
8.8%
- 74612
 
8.5%
D 71911
 
8.2%
a 68466
 
7.8%
h 49868
 
5.7%
t 49868
 
5.7%
1 49858
 
5.7%
y 49858
 
5.7%
l 49858
 
5.7%
Other values (16) 188207
21.4%

Traffic_Control
Categorical

High correlation  Imbalance 

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size583.0 KiB
10 - No control
34638 
01 - Traffic signal
30172 
02 - Stop sign
8165 
11 - Roundabout
 
992
03 - Yield sign
 
429
Other values (7)
 
215

Length

Max length23
Median length19
Mean length16.496026
Min length8

Characters and Unicode

Total characters1230785
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10 - No control
2nd row10 - No control
3rd row01 - Traffic signal
4th row01 - Traffic signal
5th row01 - Traffic signal

Common Values

ValueCountFrequency (%)
10 - No control 34638
46.4%
01 - Traffic signal 30172
40.4%
02 - Stop sign 8165
 
10.9%
11 - Roundabout 992
 
1.3%
03 - Yield sign 429
 
0.6%
12 - IPS 88
 
0.1%
99 - Other 48
 
0.1%
13 - MPS 33
 
< 0.1%
04 - Ped. crossover 24
 
< 0.1%
09 - Traffic controller 9
 
< 0.1%
Other values (2) 13
 
< 0.1%

Length

2024-10-16T22:09:48.839944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
74611
25.1%
10 34638
11.7%
no 34638
11.7%
control 34638
11.7%
traffic 30189
10.2%
01 30172
10.1%
signal 30172
10.1%
sign 8594
 
2.9%
02 8165
 
2.7%
stop 8165
 
2.7%
Other values (20) 3301
 
1.1%

Most occurring characters

ValueCountFrequency (%)
222672
18.1%
o 114139
 
9.3%
- 74611
 
6.1%
n 74405
 
6.0%
0 73450
 
6.0%
i 69384
 
5.6%
1 66915
 
5.4%
l 65262
 
5.3%
r 64941
 
5.3%
c 64865
 
5.3%
Other values (28) 340141
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1230785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
222672
18.1%
o 114139
 
9.3%
- 74611
 
6.1%
n 74405
 
6.0%
0 73450
 
6.0%
i 69384
 
5.6%
1 66915
 
5.4%
l 65262
 
5.3%
r 64941
 
5.3%
c 64865
 
5.3%
Other values (28) 340141
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1230785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
222672
18.1%
o 114139
 
9.3%
- 74611
 
6.1%
n 74405
 
6.0%
0 73450
 
6.0%
i 69384
 
5.6%
1 66915
 
5.4%
l 65262
 
5.3%
r 64941
 
5.3%
c 64865
 
5.3%
Other values (28) 340141
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1230785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
222672
18.1%
o 114139
 
9.3%
- 74611
 
6.1%
n 74405
 
6.0%
0 73450
 
6.0%
i 69384
 
5.6%
1 66915
 
5.4%
l 65262
 
5.3%
r 64941
 
5.3%
c 64865
 
5.3%
Other values (28) 340141
27.6%

Num_of_Vehicle
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8412186
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size583.0 KiB
2024-10-16T22:09:49.051036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum25
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.58651207
Coefficient of variation (CV)0.3185456
Kurtosis36.938837
Mean1.8412186
Median Absolute Deviation (MAD)0
Skewness1.5433167
Sum137377
Variance0.34399641
MonotonicityNot monotonic
2024-10-16T22:09:49.269305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 51248
68.7%
1 18127
 
24.3%
3 4425
 
5.9%
4 660
 
0.9%
5 112
 
0.2%
6 24
 
< 0.1%
7 12
 
< 0.1%
9 2
 
< 0.1%
8 1
 
< 0.1%
25 1
 
< 0.1%
ValueCountFrequency (%)
1 18127
 
24.3%
2 51248
68.7%
3 4425
 
5.9%
4 660
 
0.9%
5 112
 
0.2%
6 24
 
< 0.1%
7 12
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
25 1
 
< 0.1%
ValueCountFrequency (%)
25 1
 
< 0.1%
9 2
 
< 0.1%
8 1
 
< 0.1%
7 12
 
< 0.1%
6 24
 
< 0.1%
5 112
 
0.2%
4 660
 
0.9%
3 4425
 
5.9%
2 51248
68.7%
1 18127
 
24.3%

Num_Of_Pedestrians
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size583.0 KiB
0
73015 
1
 
1535
2
 
58
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74612
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73015
97.9%
1 1535
 
2.1%
2 58
 
0.1%
3 4
 
< 0.1%

Length

2024-10-16T22:09:49.495811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T22:09:49.741939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 73015
97.9%
1 1535
 
2.1%
2 58
 
0.1%
3 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 73015
97.9%
1 1535
 
2.1%
2 58
 
0.1%
3 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74612
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73015
97.9%
1 1535
 
2.1%
2 58
 
0.1%
3 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74612
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73015
97.9%
1 1535
 
2.1%
2 58
 
0.1%
3 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74612
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73015
97.9%
1 1535
 
2.1%
2 58
 
0.1%
3 4
 
< 0.1%

Num_of_Bicycles
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size583.0 KiB
0.0
73265 
1.0
 
1335
2.0
 
10
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters223836
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 73265
98.2%
1.0 1335
 
1.8%
2.0 10
 
< 0.1%
3.0 2
 
< 0.1%

Length

2024-10-16T22:09:49.942167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T22:09:50.189590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 73265
98.2%
1.0 1335
 
1.8%
2.0 10
 
< 0.1%
3.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 147877
66.1%
. 74612
33.3%
1 1335
 
0.6%
2 10
 
< 0.1%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 147877
66.1%
. 74612
33.3%
1 1335
 
0.6%
2 10
 
< 0.1%
3 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 147877
66.1%
. 74612
33.3%
1 1335
 
0.6%
2 10
 
< 0.1%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 147877
66.1%
. 74612
33.3%
1 1335
 
0.6%
2 10
 
< 0.1%
3 2
 
< 0.1%

Num_of_Motorcycles
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size583.0 KiB
0.0
73975 
1.0
 
628
2.0
 
8
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters223836
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 73975
99.1%
1.0 628
 
0.8%
2.0 8
 
< 0.1%
3.0 1
 
< 0.1%

Length

2024-10-16T22:09:50.395278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T22:09:50.681933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 73975
99.1%
1.0 628
 
0.8%
2.0 8
 
< 0.1%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 148587
66.4%
. 74612
33.3%
1 628
 
0.3%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 148587
66.4%
. 74612
33.3%
1 628
 
0.3%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 148587
66.4%
. 74612
33.3%
1 628
 
0.3%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 148587
66.4%
. 74612
33.3%
1 628
 
0.3%
2 8
 
< 0.1%
3 1
 
< 0.1%

Max_Injury
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size583.0 KiB

Num_of_Injuries
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23347451
Minimum0
Maximum38
Zeros61195
Zeros (%)82.0%
Negative0
Negative (%)0.0%
Memory size583.0 KiB
2024-10-16T22:09:50.977360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum38
Range38
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.58800648
Coefficient of variation (CV)2.518504
Kurtosis242.63414
Mean0.23347451
Median Absolute Deviation (MAD)0
Skewness6.6191692
Sum17420
Variance0.34575162
MonotonicityNot monotonic
2024-10-16T22:09:51.286491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 61195
82.0%
1 10497
 
14.1%
2 2193
 
2.9%
3 523
 
0.7%
4 129
 
0.2%
5 46
 
0.1%
6 18
 
< 0.1%
7 5
 
< 0.1%
8 4
 
< 0.1%
38 1
 
< 0.1%
ValueCountFrequency (%)
0 61195
82.0%
1 10497
 
14.1%
2 2193
 
2.9%
3 523
 
0.7%
4 129
 
0.2%
5 46
 
0.1%
6 18
 
< 0.1%
7 5
 
< 0.1%
8 4
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
38 1
 
< 0.1%
9 1
 
< 0.1%
8 4
 
< 0.1%
7 5
 
< 0.1%
6 18
 
< 0.1%
5 46
 
0.1%
4 129
 
0.2%
3 523
 
0.7%
2 2193
 
2.9%
1 10497
14.1%

Num_of_Minimal_Injuries
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.093202166
Minimum0
Maximum11
Zeros68879
Zeros (%)92.3%
Negative0
Negative (%)0.0%
Memory size583.0 KiB
2024-10-16T22:09:51.608757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.35866256
Coefficient of variation (CV)3.8482214
Kurtosis49.645612
Mean0.093202166
Median Absolute Deviation (MAD)0
Skewness5.3905638
Sum6954
Variance0.12863884
MonotonicityNot monotonic
2024-10-16T22:09:52.196735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 68879
92.3%
1 4779
 
6.4%
2 769
 
1.0%
3 138
 
0.2%
4 30
 
< 0.1%
5 10
 
< 0.1%
8 2
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
0 68879
92.3%
1 4779
 
6.4%
2 769
 
1.0%
3 138
 
0.2%
4 30
 
< 0.1%
5 10
 
< 0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
8 2
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%
5 10
 
< 0.1%
4 30
 
< 0.1%
3 138
 
0.2%
2 769
 
1.0%
1 4779
 
6.4%
0 68879
92.3%

Num_of_Minor_Injuries
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.093202166
Minimum0
Maximum11
Zeros68879
Zeros (%)92.3%
Negative0
Negative (%)0.0%
Memory size583.0 KiB
2024-10-16T22:09:52.564211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.35866256
Coefficient of variation (CV)3.8482214
Kurtosis49.645612
Mean0.093202166
Median Absolute Deviation (MAD)0
Skewness5.3905638
Sum6954
Variance0.12863884
MonotonicityNot monotonic
2024-10-16T22:09:52.896429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 68879
92.3%
1 4779
 
6.4%
2 769
 
1.0%
3 138
 
0.2%
4 30
 
< 0.1%
5 10
 
< 0.1%
8 2
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
0 68879
92.3%
1 4779
 
6.4%
2 769
 
1.0%
3 138
 
0.2%
4 30
 
< 0.1%
5 10
 
< 0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
8 2
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%
5 10
 
< 0.1%
4 30
 
< 0.1%
3 138
 
0.2%
2 769
 
1.0%
1 4779
 
6.4%
0 68879
92.3%

Num_of_Major_Injuries
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0099715863
Minimum0
Maximum14
Zeros73941
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size583.0 KiB
2024-10-16T22:09:53.242439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.11967469
Coefficient of variation (CV)12.00157
Kurtosis2660.2527
Mean0.0099715863
Median Absolute Deviation (MAD)0
Skewness31.279329
Sum744
Variance0.014322032
MonotonicityNot monotonic
2024-10-16T22:09:53.561472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 73941
99.1%
1 622
 
0.8%
2 39
 
0.1%
3 6
 
< 0.1%
4 3
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
0 73941
99.1%
1 622
 
0.8%
2 39
 
0.1%
3 6
 
< 0.1%
4 3
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
4 3
 
< 0.1%
3 6
 
< 0.1%
2 39
 
0.1%
1 622
 
0.8%
0 73941
99.1%

Num_of_Fatal_Injuries
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size583.0 KiB
0.0
74471 
1.0
 
132
2.0
 
8
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters223836
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 74471
99.8%
1.0 132
 
0.2%
2.0 8
 
< 0.1%
3.0 1
 
< 0.1%

Length

2024-10-16T22:09:53.994262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T22:09:54.416314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 74471
99.8%
1.0 132
 
0.2%
2.0 8
 
< 0.1%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 149083
66.6%
. 74612
33.3%
1 132
 
0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 149083
66.6%
. 74612
33.3%
1 132
 
0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 149083
66.6%
. 74612
33.3%
1 132
 
0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 149083
66.6%
. 74612
33.3%
1 132
 
0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Lat
Real number (ℝ)

High correlation  Skewed 

Distinct42389
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.29193
Minimum0
Maximum45.524921
Zeros76
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size583.0 KiB
2024-10-16T22:09:54.643316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45.253606
Q145.333451
median45.378984
Q345.418314
95-th percentile45.460853
Maximum45.524921
Range45.524921
Interquartile range (IQR)0.08486305

Descriptive statistics

Standard deviation1.843572
Coefficient of variation (CV)0.040704204
Kurtosis566.14395
Mean45.29193
Median Absolute Deviation (MAD)0.041780475
Skewness-23.802858
Sum3379321.5
Variance3.3987576
MonotonicityNot monotonic
2024-10-16T22:09:54.927348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.46414526 229
 
0.3%
45.33460787 208
 
0.3%
45.48898157 177
 
0.2%
45.46085266 158
 
0.2%
45.33417384 154
 
0.2%
45.35150226 140
 
0.2%
45.32869813 139
 
0.2%
45.27053061 136
 
0.2%
45.37040769 136
 
0.2%
45.41648492 132
 
0.2%
Other values (42379) 73003
97.8%
ValueCountFrequency (%)
0 76
0.1%
3.318485223 50
0.1%
3.341503337 5
 
< 0.1%
44.96795959 1
 
< 0.1%
44.96797999 1
 
< 0.1%
44.96979868 1
 
< 0.1%
44.97028019 1
 
< 0.1%
44.97358922 1
 
< 0.1%
44.98083128 1
 
< 0.1%
44.98106813 1
 
< 0.1%
ValueCountFrequency (%)
45.52492133 1
 
< 0.1%
45.52476088 1
 
< 0.1%
45.52470204 5
< 0.1%
45.52468914 1
 
< 0.1%
45.52464234 1
 
< 0.1%
45.52452523 1
 
< 0.1%
45.52440728 1
 
< 0.1%
45.52440692 1
 
< 0.1%
45.52440179 1
 
< 0.1%
45.52440045 1
 
< 0.1%

Long
Real number (ℝ)

High correlation 

Distinct42172
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-75.710325
Minimum-79.23729
Maximum-75.261583
Zeros0
Zeros (%)0.0%
Negative74612
Negative (%)100.0%
Memory size583.0 KiB
2024-10-16T22:09:55.210439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-79.23729
5-th percentile-75.929355
Q1-75.755176
median-75.696671
Q3-75.642484
95-th percentile-75.498398
Maximum-75.261583
Range3.975707
Interquartile range (IQR)0.11269275

Descriptive statistics

Standard deviation0.16799797
Coefficient of variation (CV)-0.0022189572
Kurtosis196.44678
Mean-75.710325
Median Absolute Deviation (MAD)0.056860655
Skewness-9.6435503
Sum-5648898.8
Variance0.028223318
MonotonicityNot monotonic
2024-10-16T22:09:55.488666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-75.54012559 229
 
0.3%
-75.69319006 208
 
0.3%
-75.47774271 177
 
0.2%
-75.48780963 158
 
0.2%
-75.70057066 154
 
0.2%
-75.76297753 140
 
0.2%
-75.74897241 139
 
0.2%
-75.74638832 136
 
0.2%
-75.66330343 136
 
0.2%
-75.60244499 133
 
0.2%
Other values (42162) 73002
97.8%
ValueCountFrequency (%)
-79.23728992 76
0.1%
-76.33938433 1
 
< 0.1%
-76.33804351 1
 
< 0.1%
-76.3378556 1
 
< 0.1%
-76.33587105 1
 
< 0.1%
-76.33458758 1
 
< 0.1%
-76.33374987 2
 
< 0.1%
-76.33372086 1
 
< 0.1%
-76.3336814 1
 
< 0.1%
-76.33313338 1
 
< 0.1%
ValueCountFrequency (%)
-75.26158294 1
< 0.1%
-75.26813064 1
< 0.1%
-75.26816498 1
< 0.1%
-75.26880567 2
< 0.1%
-75.26893514 1
< 0.1%
-75.27313672 1
< 0.1%
-75.27461775 1
< 0.1%
-75.27512905 1
< 0.1%
-75.27646836 1
< 0.1%
-75.27867728 1
< 0.1%

Interactions

2024-10-16T22:09:37.347454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:26.874142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:28.770117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:30.456544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:32.144595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:33.833907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:35.690394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:37.716907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:27.213675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:29.001767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:30.710770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:32.370054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:34.233855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:35.903367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:38.093785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:27.457457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:29.242718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:30.953367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:32.616019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:34.476404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:36.148017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:38.452503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:27.689423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:29.482194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:31.182260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:32.851491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:34.720017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:36.376515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:38.808417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:27.920853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:29.722647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:31.405959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:33.094212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:34.949666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:36.601041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:39.187766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:28.171323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:29.978135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:31.668204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:33.336571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:35.205277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:36.846586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:39.531635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:28.382697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:30.204045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:31.901105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:33.588768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:35.439776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-16T22:09:37.070686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-10-16T22:09:55.740705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Classification_Of_AccidentEnvironment_ConditionInitial_Impact_TypeLatLightLocation_TypeLongNum_Of_PedestriansNum_of_BicyclesNum_of_Fatal_InjuriesNum_of_InjuriesNum_of_Major_InjuriesNum_of_Minimal_InjuriesNum_of_Minor_InjuriesNum_of_MotorcyclesNum_of_VehicleRoad_Surface_ConditionTraffic_Control
Classification_Of_Accident1.0000.0390.1550.0040.0640.0850.0240.2160.1690.7070.1020.0770.1830.1830.1040.0290.0490.070
Environment_Condition0.0391.0000.0650.0000.0980.0530.0270.0160.0290.0090.0000.0000.0000.0000.0230.0100.3930.018
Initial_Impact_Type0.1550.0651.0000.0390.2980.4570.1190.1880.0960.0320.0250.0320.0280.0280.0390.0550.0780.206
Lat0.0040.0000.0391.0000.0290.0060.5970.0000.0000.000-0.015-0.0070.0040.0040.0000.0710.0000.000
Light0.0640.0980.2980.0291.0000.2170.0470.0250.0250.0070.0090.0000.0080.0080.0100.0130.1010.096
Location_Type0.0850.0530.4570.0060.2171.0000.1030.0570.0490.0090.0120.0050.0290.0290.0070.0340.0640.986
Long0.0240.0270.1190.5970.0470.1031.0000.0130.0160.0080.0280.0090.0140.0140.0000.0440.0170.076
Num_Of_Pedestrians0.2160.0160.1880.0000.0250.0570.0131.0000.0070.0520.0190.0110.0260.0260.0000.0040.0230.044
Num_of_Bicycles0.1690.0290.0960.0000.0250.0490.0160.0071.0000.0070.0000.0000.0170.0170.0000.0290.0400.039
Num_of_Fatal_Injuries0.7070.0090.0320.0000.0070.0090.0080.0520.0071.0000.5780.5780.5770.5770.0380.0000.0930.018
Num_of_Injuries0.1020.0000.025-0.0150.0090.0120.0280.0190.0000.5781.0000.2090.6220.6220.0000.0310.0930.000
Num_of_Major_Injuries0.0770.0000.032-0.0070.0000.0050.0090.0110.0000.5780.2091.0000.0090.0090.012-0.0310.0930.000
Num_of_Minimal_Injuries0.1830.0000.0280.0040.0080.0290.0140.0260.0170.5770.6220.0091.0001.0000.0000.0660.0570.008
Num_of_Minor_Injuries0.1830.0000.0280.0040.0080.0290.0140.0260.0170.5770.6220.0091.0001.0000.0000.0660.0570.008
Num_of_Motorcycles0.1040.0230.0390.0000.0100.0070.0000.0000.0000.0380.0000.0120.0000.0001.0000.0000.2230.014
Num_of_Vehicle0.0290.0100.0550.0710.0130.0340.0440.0040.0290.0000.031-0.0310.0660.0660.0001.0000.0120.015
Road_Surface_Condition0.0490.3930.0780.0000.1010.0640.0170.0230.0400.0930.0930.0930.0570.0570.2230.0121.0000.023
Traffic_Control0.0700.0180.2060.0000.0960.9860.0760.0440.0390.0180.0000.0000.0080.0080.0140.0150.0231.000

Missing values

2024-10-16T22:09:40.255427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-16T22:09:41.133610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-16T22:09:41.851539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Accident_DateAccident_TimeLocationLocation_TypeClassification_Of_AccidentInitial_Impact_TypeRoad_Surface_ConditionEnvironment_ConditionLightTraffic_ControlNum_of_VehicleNum_Of_PedestriansNum_of_BicyclesNum_of_MotorcyclesMax_InjuryNum_of_InjuriesNum_of_Minimal_InjuriesNum_of_Minor_InjuriesNum_of_Major_InjuriesNum_of_Fatal_InjuriesLatLong
02017/01/011:28WEST RIDGE DR btwn PARLOR PL & BERT G. ARGUE DR (__5RG32N)Midblock03 - P.D. only07 - SMV other03 - Loose snow02 - Rain07 - Dark10 - No control100.00.000.00.00.00.00.045.254481-75.931061
12017/01/013:16VANIER PKWY SB btwn DONALD ST & MCARTHUR AVE (__3Z07B5)Midblock03 - P.D. only04 - Sideswipe03 - Loose snow03 - Snow07 - Dark10 - No control200.00.000.00.00.00.00.045.428323-75.663225
22017/01/017:17OLD RICHMOND RD @ ROBERTSON RD (0000639)Intersection03 - P.D. only07 - SMV other01 - Dry05 - Drifting Snow03 - Dawn01 - Traffic signal100.00.000.00.00.00.00.045.324735-75.826700
32017/01/018:58MERIVALE RD @ WOODFIELD DR/ROYDON PL (0009776)Intersection03 - P.D. only03 - Rear end03 - Loose snow03 - Snow01 - Daylight01 - Traffic signal200.00.000.00.00.00.00.045.338011-75.725984
42017/01/0111:41BANK ST @ BELANGER AVE/LAMIRA ST (0007208)Intersection03 - P.D. only99 - Other02 - Wet01 - Clear01 - Daylight01 - Traffic signal200.00.000.00.00.00.00.045.383786-75.671842
52017/01/0112:08POULIN AVE @ RICHMOND RD (0002748)Intersection02 - Non-fatal injury03 - Rear end02 - Wet03 - Snow01 - Daylight01 - Traffic signal200.00.002 - Minor1.00.00.00.00.045.361850-75.791232
62017/01/01UnknownEASTVALE DR btwn OGILVIE RD & LOYOLA AVE (__3Z0CDR)Midblock03 - P.D. only06 - SMV unattended vehicle04 - Slush03 - Snow00 - Unknown10 - No control100.00.000.00.00.00.00.045.452156-75.594470
72017/01/0120:26WEST HUNT CLUB RD EB btwn CEDARVIEW RD & GREENBANK RD (__3Z07PT)Midblock03 - P.D. only04 - Sideswipe02 - Wet01 - Clear07 - Dark10 - No control200.00.000.00.00.00.00.045.319864-75.782571
82017/01/0120:43QUEEN MARY ST @ QUILL ST (0008259)Intersection03 - P.D. only07 - SMV other03 - Loose snow01 - Clear07 - Dark02 - Stop sign100.00.000.00.00.00.00.045.424814-75.657416
92017/01/0121:40ERINDALE DR @ BASELINE RD (0003209)Intersection03 - P.D. only07 - SMV other02 - Wet01 - Clear07 - Dark02 - Stop sign100.00.000.00.00.00.00.045.358637-75.746190
Accident_DateAccident_TimeLocationLocation_TypeClassification_Of_AccidentInitial_Impact_TypeRoad_Surface_ConditionEnvironment_ConditionLightTraffic_ControlNum_of_VehicleNum_Of_PedestriansNum_of_BicyclesNum_of_MotorcyclesMax_InjuryNum_of_InjuriesNum_of_Minimal_InjuriesNum_of_Minor_InjuriesNum_of_Major_InjuriesNum_of_Fatal_InjuriesLatLong
746022022/05/317:00HIGHWAY 417 btwn HWY417 IC129 RAMP61 & TRANSITWAY (__3Z07AY)Midblock03 - P.D. only03 - Rear end01 - Dry01 - Clear01 - Daylight10 - No control200.00.000.00.00.00.00.045.353079-75.783496
746032022/05/3114:50HIGHWAY 417 btwn HWY417 IC155 RAMP61 & HWY417 IC145 RAMP56 (__3Z070H)Midblock03 - P.D. only07 - SMV other01 - Dry01 - Clear01 - Daylight10 - No control100.00.000.00.00.00.00.045.291036-76.012286
746042022/06/019:30HWY416 IC57 RAMP24 btwn BANKFIELD RD & HIGHWAY 416 (__3Z05I1)Midblock03 - P.D. only07 - SMV other02 - Wet02 - Rain01 - Daylight10 - No control100.00.000.00.00.00.00.045.210216-75.734365
746052022/06/028:28HIGHWAY 417 btwn HWY417 IC129 RAMP25 & TRANSITWAY (__3Z07AX)Midblock03 - P.D. only03 - Rear end01 - Dry01 - Clear01 - Daylight10 - No control200.00.000.00.00.00.00.045.358515-75.772919
746062022/06/0212:38HIGHWAY 417 btwn HWY417 IC117 RAMP36 & HWY417 IC117 RAMP26 (__3Z06TK)Midblock03 - P.D. only99 - Other01 - Dry01 - Clear01 - Daylight10 - No control200.00.000.00.00.00.00.045.417398-75.658775
746072022/06/0311:52HIGHWAY 417 btwn HWY417 IC169 RAMP26 & HWY417 IC169 RAMP61 (__5SPOU0)Midblock03 - P.D. only03 - Rear end01 - Dry01 - Clear01 - Daylight10 - No control200.00.000.00.00.00.00.045.362241-76.233622
746082022/06/0312:10HIGHWAY 417 btwn HWY417 IC134 RAMP36 & HWY417 IC134 RAMP26 (__3Z08RC)Midblock03 - P.D. only07 - SMV other01 - Dry01 - Clear01 - Daylight10 - No control100.00.000.00.00.00.00.045.338743-75.841195
746092022/06/0314:50HIGHWAY 417 btwn HWY417 IC126 RAMP25 & HWY417 IC124 RAMP57 (__3Z07AK)Midblock03 - P.D. only03 - Rear end01 - Dry01 - Clear01 - Daylight10 - No control500.00.000.00.00.00.00.045.372274-75.752376
746102022/06/0314:58HIGHWAY 417 btwn HWY417 IC120B RAMP36 & HWY417 IC119B RAMP76 (__3Z08HL)Midblock03 - P.D. only03 - Rear end01 - Dry01 - Clear01 - Daylight10 - No control300.00.000.00.00.00.00.045.409077-75.691488
746112022/06/0317:05HIGHWAY 416 btwn HWY416 IC66 RAMP62 & HWY416 IC72 RAMP24 (__3Z05ZX)Midblock03 - P.D. only04 - Sideswipe01 - Dry01 - Clear01 - Daylight10 - No control200.00.000.00.00.00.00.045.276966-75.800863

Duplicate rows

Most frequently occurring

Accident_DateAccident_TimeLocationLocation_TypeClassification_Of_AccidentInitial_Impact_TypeRoad_Surface_ConditionEnvironment_ConditionLightTraffic_ControlNum_of_VehicleNum_Of_PedestriansNum_of_BicyclesNum_of_MotorcyclesNum_of_InjuriesNum_of_Minimal_InjuriesNum_of_Minor_InjuriesNum_of_Major_InjuriesNum_of_Fatal_InjuriesLatLong# duplicates
02017/09/1922:38COLONEL BY DR @ HOG'S BACK RD (0002635)Intersection03 - P.D. only05 - Turning movement01 - Dry01 - Clear07 - Dark01 - Traffic signal200.00.00.00.00.00.00.045.370006-75.6982012
12018/02/2120:49BROOKFIELD RD @ FLANNERY DR/AIRPORT PKWY RAMP 34 (0006850)Intersection03 - P.D. only04 - Sideswipe06 - Ice01 - Clear07 - Dark11 - Roundabout200.00.00.00.00.00.00.045.373718-75.6843482
22018/09/1717:50BRONSON AVE @ SUNNYSIDE AVE/UNIVERSITY RD/CARLETON U (0007209)Intersection03 - P.D. only04 - Sideswipe01 - Dry01 - Clear01 - Daylight01 - Traffic signal200.00.00.00.00.00.00.045.389919-75.6938592
32018/11/127:08OLD TENTH LINE RD/OR174 IC101 RAMP63 @ ST. JOSEPH BLVD (0009233)Intersection03 - P.D. only07 - SMV other06 - Ice03 - Snow03 - Dawn01 - Traffic signal100.00.00.00.00.00.00.045.484600-75.4981032
42019/11/0219:45BANK ST @ HERON RD (0002155)Intersection03 - P.D. only04 - Sideswipe02 - Wet02 - Rain07 - Dark01 - Traffic signal200.00.00.00.00.00.00.045.378572-75.6675522
52020/09/1912:30VANIER PKWY/CRICHTON ST @ BEECHWOOD AVE/ST. PATRICK ST (0001604)Intersection03 - P.D. only03 - Rear end01 - Dry01 - Clear01 - Daylight01 - Traffic signal200.00.00.00.00.00.00.045.438357-75.6777672
62022/05/1012:00BRIAN COBURN BLVD @ MER BLEUE RD (0014363)Intersection03 - P.D. only03 - Rear end01 - Dry01 - Clear01 - Daylight11 - Roundabout200.00.00.00.00.00.00.045.445510-75.4983982
72022/10/0315:30O'CONNOR ST @ QUEEN ST (0003139)Intersection02 - Non-fatal injury02 - Angle01 - Dry01 - Clear01 - Daylight01 - Traffic signal201.00.01.01.01.00.00.045.421395-75.6988422